Overview

Dataset statistics

Number of variables32
Number of observations99441
Missing cells17348
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.1 MiB
Average record size in memory285.3 B

Variable types

Categorical16
Numeric15
DateTime1

Alerts

order_id has a high cardinality: 99441 distinct valuesHigh cardinality
customer_id has a high cardinality: 99441 distinct valuesHigh cardinality
order_purchase_timestamp has a high cardinality: 98875 distinct valuesHigh cardinality
order_approved_at has a high cardinality: 90733 distinct valuesHigh cardinality
order_delivered_carrier_date has a high cardinality: 81018 distinct valuesHigh cardinality
order_delivered_customer_date has a high cardinality: 95664 distinct valuesHigh cardinality
order_estimated_delivery_date has a high cardinality: 459 distinct valuesHigh cardinality
product_most_frequent has a high cardinality: 31847 distinct valuesHigh cardinality
customer_unique_id has a high cardinality: 96096 distinct valuesHigh cardinality
customer_city has a high cardinality: 4119 distinct valuesHigh cardinality
product_id has a high cardinality: 31847 distinct valuesHigh cardinality
product_category_name_english has a high cardinality: 72 distinct valuesHigh cardinality
payment_value is highly overall correlated with sum_price and 2 other fieldsHigh correlation
sum_price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
sum_freight_value is highly overall correlated with payment_valueHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
product_weight_g is highly overall correlated with payment_value and 4 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
order_status is highly imbalanced (91.4%)Imbalance
payment_type is highly imbalanced (61.1%)Imbalance
order_delivered_carrier_date has 1783 (1.8%) missing valuesMissing
order_delivered_customer_date has 2965 (3.0%) missing valuesMissing
payment_sequential is highly skewed (γ1 = 23.94863702)Skewed
order_id is uniformly distributedUniform
customer_id is uniformly distributedUniform
order_purchase_timestamp is uniformly distributedUniform
order_approved_at is uniformly distributedUniform
order_delivered_carrier_date is uniformly distributedUniform
order_delivered_customer_date is uniformly distributedUniform
customer_unique_id is uniformly distributedUniform
order_id has unique valuesUnique
customer_id has unique valuesUnique
length_comment_title has 87122 (87.6%) zerosZeros
length_comment_message has 57894 (58.2%) zerosZeros
product_description_lenght has 1420 (1.4%) zerosZeros
product_photos_qty has 1420 (1.4%) zerosZeros

Reproduction

Analysis started2023-02-14 09:32:08.067965
Analysis finished2023-02-14 09:33:14.292184
Duration1 minute and 6.22 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

order_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct99441
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
e481f51cbdc54678b7cc49136f2d6af7
 
1
f01059d0d674e1282df4e8fbbe015aa2
 
1
fbc17f0f2a2125054d5ac5c22d2d5120
 
1
9373150545066777b1cd2bc20e93cf8e
 
1
917399e96f92268dfa2c0351b1b75fba
 
1
Other values (99436)
99436 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3182112
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99441 ?
Unique (%)100.0%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd row53cdb2fc8bc7dce0b6741e2150273451
3rd row47770eb9100c2d0c44946d9cf07ec65d
4th row949d5b44dbf5de918fe9c16f97b45f8a
5th rowad21c59c0840e6cb83a9ceb5573f8159

Common Values

ValueCountFrequency (%)
e481f51cbdc54678b7cc49136f2d6af7 1
 
< 0.1%
f01059d0d674e1282df4e8fbbe015aa2 1
 
< 0.1%
fbc17f0f2a2125054d5ac5c22d2d5120 1
 
< 0.1%
9373150545066777b1cd2bc20e93cf8e 1
 
< 0.1%
917399e96f92268dfa2c0351b1b75fba 1
 
< 0.1%
ed1691ef26bd8279bd5946561af1ff0d 1
 
< 0.1%
dc3006aa87f57332aaff74c57a5e094d 1
 
< 0.1%
f53eae1ce47dc68e8da117dd0d7feef1 1
 
< 0.1%
94bce2ab6f38b41d29ebbd9d755677bf 1
 
< 0.1%
632f22d24375715fbfa8c0ae2e5d35b7 1
 
< 0.1%
Other values (99431) 99431
> 99.9%

Length

2023-02-14T10:33:14.362619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e481f51cbdc54678b7cc49136f2d6af7 1
 
< 0.1%
2ce1ad82022c1ba30c2079502ac725aa 1
 
< 0.1%
949d5b44dbf5de918fe9c16f97b45f8a 1
 
< 0.1%
ad21c59c0840e6cb83a9ceb5573f8159 1
 
< 0.1%
a4591c265e18cb1dcee52889e2d8acc3 1
 
< 0.1%
136cce7faa42fdb2cefd53fdc79a6098 1
 
< 0.1%
6514b8ad8028c9f2cc2374ded245783f 1
 
< 0.1%
76c6e866289321a7c93b82b54852dc33 1
 
< 0.1%
e69bfb5eb88e0ed6a785585b27e16dbf 1
 
< 0.1%
e6ce16cb79ec1d90b1da9085a6118aeb 1
 
< 0.1%
Other values (99431) 99431
> 99.9%

Most occurring characters

ValueCountFrequency (%)
4 199814
 
6.3%
b 199618
 
6.3%
7 199334
 
6.3%
6 199306
 
6.3%
e 199225
 
6.3%
2 199124
 
6.3%
3 199011
 
6.3%
1 198902
 
6.3%
a 198879
 
6.2%
9 198822
 
6.2%
Other values (6) 1190077
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1989030
62.5%
Lowercase Letter 1193082
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 199814
10.0%
7 199334
10.0%
6 199306
10.0%
2 199124
10.0%
3 199011
10.0%
1 198902
10.0%
9 198822
10.0%
8 198629
10.0%
0 198434
10.0%
5 197654
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 199618
16.7%
e 199225
16.7%
a 198879
16.7%
f 198774
16.7%
c 198454
16.6%
d 198132
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1989030
62.5%
Latin 1193082
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 199814
10.0%
7 199334
10.0%
6 199306
10.0%
2 199124
10.0%
3 199011
10.0%
1 198902
10.0%
9 198822
10.0%
8 198629
10.0%
0 198434
10.0%
5 197654
9.9%
Latin
ValueCountFrequency (%)
b 199618
16.7%
e 199225
16.7%
a 198879
16.7%
f 198774
16.7%
c 198454
16.6%
d 198132
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3182112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 199814
 
6.3%
b 199618
 
6.3%
7 199334
 
6.3%
6 199306
 
6.3%
e 199225
 
6.3%
2 199124
 
6.3%
3 199011
 
6.3%
1 198902
 
6.3%
a 198879
 
6.2%
9 198822
 
6.2%
Other values (6) 1190077
37.4%

customer_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct99441
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
9ef432eb6251297304e76186b10a928d
 
1
413f7e58270a32396af030a075b924be
 
1
eb4350b67a0264c67e5e06a038e4afbb
 
1
622b07d262d545d16efbd4363a89cb91
 
1
c701fbfa77791abd05eef9eacf7ea7a8
 
1
Other values (99436)
99436 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3182112
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99441 ?
Unique (%)100.0%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd rowb0830fb4747a6c6d20dea0b8c802d7ef
3rd row41ce2a54c0b03bf3443c3d931a367089
4th rowf88197465ea7920adcdbec7375364d82
5th row8ab97904e6daea8866dbdbc4fb7aad2c

Common Values

ValueCountFrequency (%)
9ef432eb6251297304e76186b10a928d 1
 
< 0.1%
413f7e58270a32396af030a075b924be 1
 
< 0.1%
eb4350b67a0264c67e5e06a038e4afbb 1
 
< 0.1%
622b07d262d545d16efbd4363a89cb91 1
 
< 0.1%
c701fbfa77791abd05eef9eacf7ea7a8 1
 
< 0.1%
99ce553a3ac79b26416f2adca143760e 1
 
< 0.1%
50900ea3519ead20da341b41081736e9 1
 
< 0.1%
a4fe94a051d268fbbe8e4ca932ebc460 1
 
< 0.1%
ba712872211b52224c61d5bedfc1bfcf 1
 
< 0.1%
f8b67d327058afa39382991d7173b1d7 1
 
< 0.1%
Other values (99431) 99431
> 99.9%

Length

2023-02-14T10:33:14.492666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9ef432eb6251297304e76186b10a928d 1
 
< 0.1%
7f2178c5d771e17f507d3c1637339298 1
 
< 0.1%
f88197465ea7920adcdbec7375364d82 1
 
< 0.1%
8ab97904e6daea8866dbdbc4fb7aad2c 1
 
< 0.1%
503740e9ca751ccdda7ba28e9ab8f608 1
 
< 0.1%
ed0271e0b7da060a393796590e7b737a 1
 
< 0.1%
9bdf08b4b3b52b5526ff42d37d47f222 1
 
< 0.1%
f54a9f0e6b351c431402b8461ea51999 1
 
< 0.1%
31ad1d1b63eb9962463f764d4e6e0c9d 1
 
< 0.1%
494dded5b201313c64ed7f100595b95c 1
 
< 0.1%
Other values (99431) 99431
> 99.9%

Most occurring characters

ValueCountFrequency (%)
5 199366
 
6.3%
f 199255
 
6.3%
2 199235
 
6.3%
c 199193
 
6.3%
1 199150
 
6.3%
b 199137
 
6.3%
8 199094
 
6.3%
3 199061
 
6.3%
7 198923
 
6.3%
6 198760
 
6.2%
Other values (6) 1190938
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1988533
62.5%
Lowercase Letter 1193579
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 199366
10.0%
2 199235
10.0%
1 199150
10.0%
8 199094
10.0%
3 199061
10.0%
7 198923
10.0%
6 198760
10.0%
9 198689
10.0%
0 198310
10.0%
4 197945
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 199255
16.7%
c 199193
16.7%
b 199137
16.7%
e 198713
16.6%
a 198646
16.6%
d 198635
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1988533
62.5%
Latin 1193579
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 199366
10.0%
2 199235
10.0%
1 199150
10.0%
8 199094
10.0%
3 199061
10.0%
7 198923
10.0%
6 198760
10.0%
9 198689
10.0%
0 198310
10.0%
4 197945
10.0%
Latin
ValueCountFrequency (%)
f 199255
16.7%
c 199193
16.7%
b 199137
16.7%
e 198713
16.6%
a 198646
16.6%
d 198635
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3182112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 199366
 
6.3%
f 199255
 
6.3%
2 199235
 
6.3%
c 199193
 
6.3%
1 199150
 
6.3%
b 199137
 
6.3%
8 199094
 
6.3%
3 199061
 
6.3%
7 198923
 
6.3%
6 198760
 
6.2%
Other values (6) 1190938
37.4%

order_status
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
delivered
96478 
shipped
 
1107
canceled
 
625
unavailable
 
609
invoiced
 
314
Other values (3)
 
308

Length

Max length11
Median length9
Mean length8.9834475
Min length7

Characters and Unicode

Total characters893323
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 96478
97.0%
shipped 1107
 
1.1%
canceled 625
 
0.6%
unavailable 609
 
0.6%
invoiced 314
 
0.3%
processing 301
 
0.3%
created 5
 
< 0.1%
approved 2
 
< 0.1%

Length

2023-02-14T10:33:14.643617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T10:33:14.821853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
delivered 96478
97.0%
shipped 1107
 
1.1%
canceled 625
 
0.6%
unavailable 609
 
0.6%
invoiced 314
 
0.3%
processing 301
 
0.3%
created 5
 
< 0.1%
approved 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 293027
32.8%
d 195009
21.8%
i 99123
 
11.1%
l 98321
 
11.0%
v 97403
 
10.9%
r 96786
 
10.8%
p 2519
 
0.3%
a 2459
 
0.3%
c 1870
 
0.2%
n 1849
 
0.2%
Other values (7) 4957
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 893323
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 293027
32.8%
d 195009
21.8%
i 99123
 
11.1%
l 98321
 
11.0%
v 97403
 
10.9%
r 96786
 
10.8%
p 2519
 
0.3%
a 2459
 
0.3%
c 1870
 
0.2%
n 1849
 
0.2%
Other values (7) 4957
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 893323
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 293027
32.8%
d 195009
21.8%
i 99123
 
11.1%
l 98321
 
11.0%
v 97403
 
10.9%
r 96786
 
10.8%
p 2519
 
0.3%
a 2459
 
0.3%
c 1870
 
0.2%
n 1849
 
0.2%
Other values (7) 4957
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 893323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 293027
32.8%
d 195009
21.8%
i 99123
 
11.1%
l 98321
 
11.0%
v 97403
 
10.9%
r 96786
 
10.8%
p 2519
 
0.3%
a 2459
 
0.3%
c 1870
 
0.2%
n 1849
 
0.2%
Other values (7) 4957
 
0.6%

order_purchase_timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct98875
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2018-04-11 10:48:14
 
3
2018-07-28 13:11:22
 
3
2017-11-20 10:59:08
 
3
2018-08-02 12:05:26
 
3
2018-08-02 12:06:09
 
3
Other values (98870)
99426 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1889379
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98319 ?
Unique (%)98.9%

Sample

1st row2017-10-02 10:56:33
2nd row2018-07-24 20:41:37
3rd row2018-08-08 08:38:49
4th row2017-11-18 19:28:06
5th row2018-02-13 21:18:39

Common Values

ValueCountFrequency (%)
2018-04-11 10:48:14 3
 
< 0.1%
2018-07-28 13:11:22 3
 
< 0.1%
2017-11-20 10:59:08 3
 
< 0.1%
2018-08-02 12:05:26 3
 
< 0.1%
2018-08-02 12:06:09 3
 
< 0.1%
2018-06-01 13:39:44 3
 
< 0.1%
2018-03-31 15:08:21 3
 
< 0.1%
2018-02-19 15:37:47 3
 
< 0.1%
2018-08-02 12:06:07 3
 
< 0.1%
2017-11-20 11:46:30 3
 
< 0.1%
Other values (98865) 99411
> 99.9%

Length

2023-02-14T10:33:14.966933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-24 1176
 
0.6%
2017-11-25 499
 
0.3%
2017-11-27 403
 
0.2%
2017-11-26 391
 
0.2%
2017-11-28 380
 
0.2%
2018-05-07 372
 
0.2%
2018-08-06 372
 
0.2%
2018-08-07 370
 
0.2%
2018-05-14 364
 
0.2%
2018-05-16 357
 
0.2%
Other values (51442) 194198
97.6%

Most occurring characters

ValueCountFrequency (%)
1 307586
16.3%
0 306287
16.2%
2 242536
12.8%
- 198882
10.5%
: 198882
10.5%
8 103570
 
5.5%
99441
 
5.3%
7 92231
 
4.9%
3 87960
 
4.7%
4 80406
 
4.3%
Other values (3) 171598
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1392174
73.7%
Dash Punctuation 198882
 
10.5%
Other Punctuation 198882
 
10.5%
Space Separator 99441
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 307586
22.1%
0 306287
22.0%
2 242536
17.4%
8 103570
 
7.4%
7 92231
 
6.6%
3 87960
 
6.3%
4 80406
 
5.8%
5 80169
 
5.8%
6 47041
 
3.4%
9 44388
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 198882
100.0%
Other Punctuation
ValueCountFrequency (%)
: 198882
100.0%
Space Separator
ValueCountFrequency (%)
99441
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1889379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 307586
16.3%
0 306287
16.2%
2 242536
12.8%
- 198882
10.5%
: 198882
10.5%
8 103570
 
5.5%
99441
 
5.3%
7 92231
 
4.9%
3 87960
 
4.7%
4 80406
 
4.3%
Other values (3) 171598
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1889379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 307586
16.3%
0 306287
16.2%
2 242536
12.8%
- 198882
10.5%
: 198882
10.5%
8 103570
 
5.5%
99441
 
5.3%
7 92231
 
4.9%
3 87960
 
4.7%
4 80406
 
4.3%
Other values (3) 171598
9.1%

order_approved_at
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct90733
Distinct (%)91.4%
Missing160
Missing (%)0.2%
Memory size1.5 MiB
2018-02-27 04:31:10
 
9
2018-02-06 05:31:52
 
7
2017-11-07 07:30:38
 
7
2017-12-05 10:30:42
 
7
2018-07-05 16:33:01
 
7
Other values (90728)
99244 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1886339
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83688 ?
Unique (%)84.3%

Sample

1st row2017-10-02 11:07:15
2nd row2018-07-26 03:24:27
3rd row2018-08-08 08:55:23
4th row2017-11-18 19:45:59
5th row2018-02-13 22:20:29

Common Values

ValueCountFrequency (%)
2018-02-27 04:31:10 9
 
< 0.1%
2018-02-06 05:31:52 7
 
< 0.1%
2017-11-07 07:30:38 7
 
< 0.1%
2017-12-05 10:30:42 7
 
< 0.1%
2018-07-05 16:33:01 7
 
< 0.1%
2017-11-07 07:30:29 7
 
< 0.1%
2018-01-10 10:32:03 7
 
< 0.1%
2018-02-27 04:31:01 7
 
< 0.1%
2018-07-23 12:32:17 6
 
< 0.1%
2018-03-27 04:08:34 6
 
< 0.1%
Other values (90723) 99211
99.8%
(Missing) 160
 
0.2%

Length

2023-02-14T10:33:15.090720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-24 990
 
0.5%
2017-11-24 799
 
0.4%
2017-11-25 754
 
0.4%
2018-07-05 697
 
0.4%
2017-11-28 506
 
0.3%
2018-08-07 444
 
0.2%
2018-05-08 426
 
0.2%
2018-08-20 426
 
0.2%
2017-12-05 426
 
0.2%
2018-01-22 408
 
0.2%
Other values (42347) 192686
97.0%

Most occurring characters

ValueCountFrequency (%)
0 318624
16.9%
1 305309
16.2%
2 241070
12.8%
- 198562
10.5%
: 198562
10.5%
99281
 
5.3%
8 98283
 
5.2%
5 95612
 
5.1%
3 93087
 
4.9%
7 87979
 
4.7%
Other values (3) 149970
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1389934
73.7%
Dash Punctuation 198562
 
10.5%
Other Punctuation 198562
 
10.5%
Space Separator 99281
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 318624
22.9%
1 305309
22.0%
2 241070
17.3%
8 98283
 
7.1%
5 95612
 
6.9%
3 93087
 
6.7%
7 87979
 
6.3%
4 68887
 
5.0%
6 42734
 
3.1%
9 38349
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 198562
100.0%
Other Punctuation
ValueCountFrequency (%)
: 198562
100.0%
Space Separator
ValueCountFrequency (%)
99281
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1886339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 318624
16.9%
1 305309
16.2%
2 241070
12.8%
- 198562
10.5%
: 198562
10.5%
99281
 
5.3%
8 98283
 
5.2%
5 95612
 
5.1%
3 93087
 
4.9%
7 87979
 
4.7%
Other values (3) 149970
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1886339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 318624
16.9%
1 305309
16.2%
2 241070
12.8%
- 198562
10.5%
: 198562
10.5%
99281
 
5.3%
8 98283
 
5.2%
5 95612
 
5.1%
3 93087
 
4.9%
7 87979
 
4.7%
Other values (3) 149970
8.0%

order_delivered_carrier_date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct81018
Distinct (%)83.0%
Missing1783
Missing (%)1.8%
Memory size1.5 MiB
2018-05-09 15:48:00
 
47
2018-05-10 18:29:00
 
32
2018-05-07 12:31:00
 
21
2018-07-24 16:07:00
 
16
2018-05-02 15:15:00
 
16
Other values (81013)
97526 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1855502
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70926 ?
Unique (%)72.6%

Sample

1st row2017-10-04 19:55:00
2nd row2018-07-26 14:31:00
3rd row2018-08-08 13:50:00
4th row2017-11-22 13:39:59
5th row2018-02-14 19:46:34

Common Values

ValueCountFrequency (%)
2018-05-09 15:48:00 47
 
< 0.1%
2018-05-10 18:29:00 32
 
< 0.1%
2018-05-07 12:31:00 21
 
< 0.1%
2018-07-24 16:07:00 16
 
< 0.1%
2018-05-02 15:15:00 16
 
< 0.1%
2018-07-17 14:16:00 15
 
< 0.1%
2018-05-16 13:44:00 15
 
< 0.1%
2018-08-03 15:10:00 15
 
< 0.1%
2018-08-08 15:01:00 15
 
< 0.1%
2018-05-17 15:06:00 14
 
< 0.1%
Other values (81008) 97452
98.0%
(Missing) 1783
 
1.8%

Length

2023-02-14T10:33:15.221947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-28 707
 
0.4%
2017-11-27 673
 
0.3%
2017-11-29 566
 
0.3%
2018-02-27 523
 
0.3%
2018-03-27 511
 
0.3%
2018-08-06 510
 
0.3%
2017-11-30 489
 
0.3%
2018-08-13 472
 
0.2%
2018-05-15 451
 
0.2%
2018-05-03 450
 
0.2%
Other values (37539) 189964
97.3%

Most occurring characters

ValueCountFrequency (%)
0 338915
18.3%
1 288924
15.6%
2 230289
12.4%
- 195316
10.5%
: 195316
10.5%
8 103165
 
5.6%
97658
 
5.3%
7 88752
 
4.8%
3 81979
 
4.4%
4 77011
 
4.2%
Other values (3) 158177
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1367212
73.7%
Dash Punctuation 195316
 
10.5%
Other Punctuation 195316
 
10.5%
Space Separator 97658
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 338915
24.8%
1 288924
21.1%
2 230289
16.8%
8 103165
 
7.5%
7 88752
 
6.5%
3 81979
 
6.0%
4 77011
 
5.6%
5 74722
 
5.5%
6 42928
 
3.1%
9 40527
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 195316
100.0%
Other Punctuation
ValueCountFrequency (%)
: 195316
100.0%
Space Separator
ValueCountFrequency (%)
97658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1855502
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 338915
18.3%
1 288924
15.6%
2 230289
12.4%
- 195316
10.5%
: 195316
10.5%
8 103165
 
5.6%
97658
 
5.3%
7 88752
 
4.8%
3 81979
 
4.4%
4 77011
 
4.2%
Other values (3) 158177
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1855502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 338915
18.3%
1 288924
15.6%
2 230289
12.4%
- 195316
10.5%
: 195316
10.5%
8 103165
 
5.6%
97658
 
5.3%
7 88752
 
4.8%
3 81979
 
4.4%
4 77011
 
4.2%
Other values (3) 158177
8.5%

order_delivered_customer_date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct95664
Distinct (%)99.2%
Missing2965
Missing (%)3.0%
Memory size1.5 MiB
2018-07-24 21:36:42
 
3
2018-05-08 23:38:46
 
3
2018-05-14 20:02:44
 
3
2017-12-02 00:26:45
 
3
2018-05-08 19:36:48
 
3
Other values (95659)
96461 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1833044
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94860 ?
Unique (%)98.3%

Sample

1st row2017-10-10 21:25:13
2nd row2018-08-07 15:27:45
3rd row2018-08-17 18:06:29
4th row2017-12-02 00:28:42
5th row2018-02-16 18:17:02

Common Values

ValueCountFrequency (%)
2018-07-24 21:36:42 3
 
< 0.1%
2018-05-08 23:38:46 3
 
< 0.1%
2018-05-14 20:02:44 3
 
< 0.1%
2017-12-02 00:26:45 3
 
< 0.1%
2018-05-08 19:36:48 3
 
< 0.1%
2016-10-27 17:32:07 3
 
< 0.1%
2018-02-14 21:09:19 3
 
< 0.1%
2017-06-19 18:47:51 3
 
< 0.1%
2018-03-16 13:28:28 2
 
< 0.1%
2018-07-06 16:32:39 2
 
< 0.1%
Other values (95654) 96448
97.0%
(Missing) 2965
 
3.0%

Length

2023-02-14T10:33:15.357462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-27 446
 
0.2%
2018-08-13 442
 
0.2%
2018-05-14 434
 
0.2%
2018-05-21 431
 
0.2%
2018-05-18 425
 
0.2%
2018-04-11 413
 
0.2%
2017-12-11 412
 
0.2%
2018-07-03 410
 
0.2%
2018-05-03 409
 
0.2%
2017-06-19 405
 
0.2%
Other values (41734) 188725
97.8%

Most occurring characters

ValueCountFrequency (%)
1 282611
15.4%
0 281259
15.3%
2 243609
13.3%
- 192952
10.5%
: 192952
10.5%
8 113512
6.2%
96476
 
5.3%
3 89135
 
4.9%
7 88999
 
4.9%
4 83444
 
4.6%
Other values (3) 168095
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1350664
73.7%
Dash Punctuation 192952
 
10.5%
Other Punctuation 192952
 
10.5%
Space Separator 96476
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 282611
20.9%
0 281259
20.8%
2 243609
18.0%
8 113512
8.4%
3 89135
 
6.6%
7 88999
 
6.6%
4 83444
 
6.2%
5 78170
 
5.8%
6 48254
 
3.6%
9 41671
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 192952
100.0%
Other Punctuation
ValueCountFrequency (%)
: 192952
100.0%
Space Separator
ValueCountFrequency (%)
96476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1833044
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 282611
15.4%
0 281259
15.3%
2 243609
13.3%
- 192952
10.5%
: 192952
10.5%
8 113512
6.2%
96476
 
5.3%
3 89135
 
4.9%
7 88999
 
4.9%
4 83444
 
4.6%
Other values (3) 168095
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1833044
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 282611
15.4%
0 281259
15.3%
2 243609
13.3%
- 192952
10.5%
: 192952
10.5%
8 113512
6.2%
96476
 
5.3%
3 89135
 
4.9%
7 88999
 
4.9%
4 83444
 
4.6%
Other values (3) 168095
9.2%
Distinct459
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2017-12-20 00:00:00
 
522
2018-03-12 00:00:00
 
516
2018-05-29 00:00:00
 
513
2018-03-13 00:00:00
 
513
2018-02-14 00:00:00
 
507
Other values (454)
96870 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1889379
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st row2017-10-18 00:00:00
2nd row2018-08-13 00:00:00
3rd row2018-09-04 00:00:00
4th row2017-12-15 00:00:00
5th row2018-02-26 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-20 00:00:00 522
 
0.5%
2018-03-12 00:00:00 516
 
0.5%
2018-05-29 00:00:00 513
 
0.5%
2018-03-13 00:00:00 513
 
0.5%
2018-02-14 00:00:00 507
 
0.5%
2017-12-18 00:00:00 493
 
0.5%
2018-05-28 00:00:00 492
 
0.5%
2018-03-06 00:00:00 492
 
0.5%
2018-02-06 00:00:00 491
 
0.5%
2018-04-12 00:00:00 490
 
0.5%
Other values (449) 94412
94.9%

Length

2023-02-14T10:33:15.492131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 99441
50.0%
2017-12-20 522
 
0.3%
2018-03-12 516
 
0.3%
2018-05-29 513
 
0.3%
2018-03-13 513
 
0.3%
2018-02-14 507
 
0.3%
2017-12-18 493
 
0.2%
2018-05-28 492
 
0.2%
2018-03-06 492
 
0.2%
2018-02-06 491
 
0.2%
Other values (450) 94902
47.7%

Most occurring characters

ValueCountFrequency (%)
0 822625
43.5%
- 198882
 
10.5%
: 198882
 
10.5%
1 170040
 
9.0%
2 155085
 
8.2%
99441
 
5.3%
8 82640
 
4.4%
7 60296
 
3.2%
3 26615
 
1.4%
5 20381
 
1.1%
Other values (3) 54492
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1392174
73.7%
Dash Punctuation 198882
 
10.5%
Other Punctuation 198882
 
10.5%
Space Separator 99441
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 822625
59.1%
1 170040
 
12.2%
2 155085
 
11.1%
8 82640
 
5.9%
7 60296
 
4.3%
3 26615
 
1.9%
5 20381
 
1.5%
6 19355
 
1.4%
4 18619
 
1.3%
9 16518
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 198882
100.0%
Other Punctuation
ValueCountFrequency (%)
: 198882
100.0%
Space Separator
ValueCountFrequency (%)
99441
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1889379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 822625
43.5%
- 198882
 
10.5%
: 198882
 
10.5%
1 170040
 
9.0%
2 155085
 
8.2%
99441
 
5.3%
8 82640
 
4.4%
7 60296
 
3.2%
3 26615
 
1.4%
5 20381
 
1.1%
Other values (3) 54492
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1889379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 822625
43.5%
- 198882
 
10.5%
: 198882
 
10.5%
1 170040
 
9.0%
2 155085
 
8.2%
99441
 
5.3%
8 82640
 
4.4%
7 60296
 
3.2%
3 26615
 
1.4%
5 20381
 
1.1%
Other values (3) 54492
 
2.9%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing768
Missing (%)0.8%
Memory size1.5 MiB
5.0
57007 
4.0
19038 
1.0
11363 
3.0
8134 
2.0
 
3131

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters296019
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 57007
57.3%
4.0 19038
 
19.1%
1.0 11363
 
11.4%
3.0 8134
 
8.2%
2.0 3131
 
3.1%
(Missing) 768
 
0.8%

Length

2023-02-14T10:33:15.621004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T10:33:15.755041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
5.0 57007
57.8%
4.0 19038
 
19.3%
1.0 11363
 
11.5%
3.0 8134
 
8.2%
2.0 3131
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 98673
33.3%
0 98673
33.3%
5 57007
19.3%
4 19038
 
6.4%
1 11363
 
3.8%
3 8134
 
2.7%
2 3131
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 197346
66.7%
Other Punctuation 98673
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98673
50.0%
5 57007
28.9%
4 19038
 
9.6%
1 11363
 
5.8%
3 8134
 
4.1%
2 3131
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 98673
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 296019
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 98673
33.3%
0 98673
33.3%
5 57007
19.3%
4 19038
 
6.4%
1 11363
 
3.8%
3 8134
 
2.7%
2 3131
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 296019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 98673
33.3%
0 98673
33.3%
5 57007
19.3%
4 19038
 
6.4%
1 11363
 
3.8%
3 8134
 
2.7%
2 3131
 
1.1%

length_comment_title
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing768
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.3783608
Minimum0
Maximum26
Zeros87122
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:15.890172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3584435
Coefficient of variation (CV)3.1620483
Kurtosis11.679804
Mean1.3783608
Median Absolute Deviation (MAD)0
Skewness3.4581046
Sum136007
Variance18.99603
MonotonicityNot monotonic
2023-02-14T10:33:16.027883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 87122
87.6%
9 2000
 
2.0%
5 1117
 
1.1%
15 870
 
0.9%
3 703
 
0.7%
10 575
 
0.6%
13 489
 
0.5%
17 482
 
0.5%
25 429
 
0.4%
14 399
 
0.4%
Other values (17) 4487
 
4.5%
(Missing) 768
 
0.8%
ValueCountFrequency (%)
0 87122
87.6%
1 164
 
0.2%
2 253
 
0.3%
3 703
 
0.7%
4 178
 
0.2%
5 1117
 
1.1%
6 243
 
0.2%
7 388
 
0.4%
8 342
 
0.3%
9 2000
 
2.0%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 429
0.4%
24 221
0.2%
23 213
0.2%
22 213
0.2%
21 239
0.2%
20 390
0.4%
19 268
0.3%
18 301
0.3%
17 482
0.5%

length_comment_message
Real number (ℝ)

Distinct209
Distinct (%)0.2%
Missing768
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean28.274888
Minimum0
Maximum208
Zeros57894
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:16.208369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q342
95-th percentile146
Maximum208
Range208
Interquartile range (IQR)42

Descriptive statistics

Standard deviation48.294605
Coefficient of variation (CV)1.7080388
Kurtosis3.3124722
Mean28.274888
Median Absolute Deviation (MAD)0
Skewness1.9820518
Sum2789968
Variance2332.3689
MonotonicityNot monotonic
2023-02-14T10:33:16.383415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57894
58.2%
9 1006
 
1.0%
200 594
 
0.6%
5 558
 
0.6%
3 514
 
0.5%
26 503
 
0.5%
20 475
 
0.5%
10 469
 
0.5%
34 464
 
0.5%
31 451
 
0.5%
Other values (199) 35745
35.9%
(Missing) 768
 
0.8%
ValueCountFrequency (%)
0 57894
58.2%
1 97
 
0.1%
2 197
 
0.2%
3 514
 
0.5%
4 101
 
0.1%
5 558
 
0.6%
6 205
 
0.2%
7 236
 
0.2%
8 244
 
0.2%
9 1006
 
1.0%
ValueCountFrequency (%)
208 1
 
< 0.1%
207 1
 
< 0.1%
206 1
 
< 0.1%
205 1
 
< 0.1%
204 15
 
< 0.1%
203 17
 
< 0.1%
202 12
 
< 0.1%
201 22
 
< 0.1%
200 594
0.6%
199 334
0.3%
Distinct97966
Distinct (%)99.3%
Missing768
Missing (%)0.8%
Memory size1.5 MiB
Minimum2016-10-07 18:32:28
Maximum2018-10-29 12:27:35
2023-02-14T10:33:16.556239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:16.716983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

payment_type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.5 MiB
credit_card
74259 
boleto
19784 
credit_card,voucher
 
2245
voucher
 
1621
debit_card
 
1527
Other values (2)
 
4

Length

Max length22
Median length11
Mean length10.10539
Min length6

Characters and Unicode

Total characters1004880
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcredit_card,voucher
2nd rowboleto
3rd rowcredit_card
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 74259
74.7%
boleto 19784
 
19.9%
credit_card,voucher 2245
 
2.3%
voucher 1621
 
1.6%
debit_card 1527
 
1.5%
not_defined 3
 
< 0.1%
credit_card,debit_card 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-02-14T10:33:16.893047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T10:33:17.073614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 74259
74.7%
boleto 19784
 
19.9%
credit_card,voucher 2245
 
2.3%
voucher 1621
 
1.6%
debit_card 1527
 
1.5%
not_defined 3
 
< 0.1%
credit_card,debit_card 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 158404
15.8%
r 158404
15.8%
d 156072
15.5%
e 101689
10.1%
t 97820
9.7%
i 78036
7.8%
_ 78036
7.8%
a 78033
7.8%
o 43437
 
4.3%
b 21312
 
2.1%
Other values (7) 33637
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 924598
92.0%
Connector Punctuation 78036
 
7.8%
Other Punctuation 2246
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 158404
17.1%
r 158404
17.1%
d 156072
16.9%
e 101689
11.0%
t 97820
10.6%
i 78036
8.4%
a 78033
8.4%
o 43437
 
4.7%
b 21312
 
2.3%
l 19784
 
2.1%
Other values (5) 11607
 
1.3%
Connector Punctuation
ValueCountFrequency (%)
_ 78036
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 924598
92.0%
Common 80282
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 158404
17.1%
r 158404
17.1%
d 156072
16.9%
e 101689
11.0%
t 97820
10.6%
i 78036
8.4%
a 78033
8.4%
o 43437
 
4.7%
b 21312
 
2.3%
l 19784
 
2.1%
Other values (5) 11607
 
1.3%
Common
ValueCountFrequency (%)
_ 78036
97.2%
, 2246
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1004880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 158404
15.8%
r 158404
15.8%
d 156072
15.5%
e 101689
10.1%
t 97820
9.7%
i 78036
7.8%
_ 78036
7.8%
a 78033
7.8%
o 43437
 
4.3%
b 21312
 
2.1%
Other values (7) 33637
 
3.3%

payment_sequential
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.0447104
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:17.211605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38116563
Coefficient of variation (CV)0.36485292
Kurtosis1008.6736
Mean1.0447104
Median Absolute Deviation (MAD)0
Skewness23.948637
Sum103886
Variance0.14528724
MonotonicityNot monotonic
2023-02-14T10:33:17.335817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 96479
97.0%
2 2382
 
2.4%
3 301
 
0.3%
4 108
 
0.1%
5 52
 
0.1%
6 36
 
< 0.1%
7 28
 
< 0.1%
8 11
 
< 0.1%
9 9
 
< 0.1%
12 8
 
< 0.1%
Other values (10) 26
 
< 0.1%
ValueCountFrequency (%)
1 96479
97.0%
2 2382
 
2.4%
3 301
 
0.3%
4 108
 
0.1%
5 52
 
0.1%
6 36
 
< 0.1%
7 28
 
< 0.1%
8 11
 
< 0.1%
9 9
 
< 0.1%
10 5
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
26 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
19 2
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 8
< 0.1%
11 8
< 0.1%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9305209
Minimum0
Maximum24
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:17.476212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7156847
Coefficient of variation (CV)0.92669008
Kurtosis2.3525407
Mean2.9305209
Median Absolute Deviation (MAD)1
Skewness1.5994122
Sum291411
Variance7.3749431
MonotonicityNot monotonic
2023-02-14T10:33:17.617092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 48268
48.5%
2 12363
 
12.4%
3 10429
 
10.5%
4 7070
 
7.1%
10 5315
 
5.3%
5 5227
 
5.3%
8 4251
 
4.3%
6 3908
 
3.9%
7 1622
 
1.6%
9 644
 
0.6%
Other values (14) 343
 
0.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 48268
48.5%
2 12363
 
12.4%
3 10429
 
10.5%
4 7070
 
7.1%
5 5227
 
5.3%
6 3908
 
3.9%
7 1622
 
1.6%
8 4251
 
4.3%
9 644
 
0.6%
ValueCountFrequency (%)
24 18
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 3
 
< 0.1%
20 17
 
< 0.1%
18 27
 
< 0.1%
17 8
 
< 0.1%
16 5
 
< 0.1%
15 74
0.1%
14 15
 
< 0.1%

payment_value
Real number (ℝ)

Distinct27979
Distinct (%)28.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean160.99027
Minimum0
Maximum13664.08
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:17.769539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.38
Q162.01
median105.29
Q3176.97
95-th percentile452.9875
Maximum13664.08
Range13664.08
Interquartile range (IQR)114.96

Descriptive statistics

Standard deviation221.95126
Coefficient of variation (CV)1.3786626
Kurtosis233.40652
Mean160.99027
Median Absolute Deviation (MAD)51.61
Skewness9.1501694
Sum16008872
Variance49262.36
MonotonicityNot monotonic
2023-02-14T10:33:17.937742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.57 254
 
0.3%
35 169
 
0.2%
73.34 163
 
0.2%
116.94 132
 
0.1%
56.78 124
 
0.1%
107.78 121
 
0.1%
65 117
 
0.1%
86.15 107
 
0.1%
99.9 106
 
0.1%
67.5 105
 
0.1%
Other values (27969) 98042
98.6%
ValueCountFrequency (%)
0 3
< 0.1%
9.59 1
 
< 0.1%
10.07 1
 
< 0.1%
10.89 1
 
< 0.1%
11.56 1
 
< 0.1%
11.62 1
 
< 0.1%
11.63 2
< 0.1%
12.22 1
 
< 0.1%
12.28 1
 
< 0.1%
12.39 1
 
< 0.1%
ValueCountFrequency (%)
13664.08 1
< 0.1%
7274.88 1
< 0.1%
6929.31 1
< 0.1%
6922.21 1
< 0.1%
6726.66 1
< 0.1%
6081.54 1
< 0.1%
4950.34 1
< 0.1%
4809.44 1
< 0.1%
4764.34 1
< 0.1%
4681.78 1
< 0.1%
Distinct31847
Distinct (%)32.3%
Missing775
Missing (%)0.8%
Memory size1.5 MiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
429
99a4788cb24856965c36a24e339b6058
 
427
422879e10f46682990de24d770e7f83d
 
339
d1c427060a0f73f6b889a5c7c61f2ac4
 
311
53b36df67ebb7c41585e8d54d6772e08
 
303
Other values (31842)
96857 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3157312
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18956 ?
Unique (%)19.2%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row595fac2a385ac33a80bd5114aec74eb8
3rd rowaa4383b373c6aca5d8797843e5594415
4th rowd0b61bfb1de832b15ba9d266ca96e5b0
5th row65266b2da20d04dbe00c5c2d3bb7859e

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 429
 
0.4%
99a4788cb24856965c36a24e339b6058 427
 
0.4%
422879e10f46682990de24d770e7f83d 339
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 311
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 303
 
0.3%
389d119b48cf3043d311335e499d9c6b 299
 
0.3%
368c6c730842d78016ad823897a372db 285
 
0.3%
154e7e31ebfa092203795c972e5804a6 269
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 264
 
0.3%
2b4609f8948be18874494203496bc318 258
 
0.3%
Other values (31837) 95482
96.0%
(Missing) 775
 
0.8%

Length

2023-02-14T10:33:18.089061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 429
 
0.4%
99a4788cb24856965c36a24e339b6058 427
 
0.4%
422879e10f46682990de24d770e7f83d 339
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 311
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 303
 
0.3%
389d119b48cf3043d311335e499d9c6b 299
 
0.3%
368c6c730842d78016ad823897a372db 285
 
0.3%
154e7e31ebfa092203795c972e5804a6 269
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 264
 
0.3%
2b4609f8948be18874494203496bc318 258
 
0.3%
Other values (31837) 95482
96.8%

Most occurring characters

ValueCountFrequency (%)
3 202985
 
6.4%
9 200585
 
6.4%
8 199130
 
6.3%
e 198981
 
6.3%
7 198253
 
6.3%
a 198185
 
6.3%
4 198184
 
6.3%
0 197884
 
6.3%
c 197489
 
6.3%
5 196954
 
6.2%
Other values (6) 1168682
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982405
62.8%
Lowercase Letter 1174907
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 202985
10.2%
9 200585
10.1%
8 199130
10.0%
7 198253
10.0%
4 198184
10.0%
0 197884
10.0%
5 196954
9.9%
2 196887
9.9%
6 196022
9.9%
1 195521
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 198981
16.9%
a 198185
16.9%
c 197489
16.8%
b 195448
16.6%
d 193791
16.5%
f 191013
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1982405
62.8%
Latin 1174907
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 202985
10.2%
9 200585
10.1%
8 199130
10.0%
7 198253
10.0%
4 198184
10.0%
0 197884
10.0%
5 196954
9.9%
2 196887
9.9%
6 196022
9.9%
1 195521
9.9%
Latin
ValueCountFrequency (%)
e 198981
16.9%
a 198185
16.9%
c 197489
16.8%
b 195448
16.6%
d 193791
16.5%
f 191013
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3157312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 202985
 
6.4%
9 200585
 
6.4%
8 199130
 
6.3%
e 198981
 
6.3%
7 198253
 
6.3%
a 198185
 
6.3%
4 198184
 
6.3%
0 197884
 
6.3%
c 197489
 
6.3%
5 196954
 
6.2%
Other values (6) 1168682
37.0%

nb_items
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing775
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.1417307
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:18.210639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.53845237
Coefficient of variation (CV)0.47161067
Kurtosis114.85034
Mean1.1417307
Median Absolute Deviation (MAD)0
Skewness7.5270056
Sum112650
Variance0.28993096
MonotonicityNot monotonic
2023-02-14T10:33:18.337112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 88863
89.4%
2 7516
 
7.6%
3 1322
 
1.3%
4 505
 
0.5%
5 204
 
0.2%
6 198
 
0.2%
7 22
 
< 0.1%
10 8
 
< 0.1%
8 8
 
< 0.1%
12 5
 
< 0.1%
Other values (7) 15
 
< 0.1%
(Missing) 775
 
0.8%
ValueCountFrequency (%)
1 88863
89.4%
2 7516
 
7.6%
3 1322
 
1.3%
4 505
 
0.5%
5 204
 
0.2%
6 198
 
0.2%
7 22
 
< 0.1%
8 8
 
< 0.1%
9 3
 
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
< 0.1%
11 4
< 0.1%
10 8
< 0.1%
9 3
 
< 0.1%
8 8
< 0.1%

sum_price
Real number (ℝ)

Distinct7761
Distinct (%)7.9%
Missing775
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean137.75408
Minimum0.85
Maximum13440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:18.506093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile19
Q145.9
median86.9
Q3149.9
95-th percentile399.9
Maximum13440
Range13439.15
Interquartile range (IQR)104

Descriptive statistics

Standard deviation210.64515
Coefficient of variation (CV)1.5291391
Kurtosis266.06694
Mean137.75408
Median Absolute Deviation (MAD)47.9
Skewness9.7277407
Sum13591644
Variance44371.377
MonotonicityNot monotonic
2023-02-14T10:33:18.684506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 1723
 
1.7%
69.9 1605
 
1.6%
49.9 1420
 
1.4%
89.9 1248
 
1.3%
99.9 1191
 
1.2%
79.9 1009
 
1.0%
39.9 978
 
1.0%
29.9 964
 
1.0%
19.9 915
 
0.9%
29.99 872
 
0.9%
Other values (7751) 86741
87.2%
ValueCountFrequency (%)
0.85 2
< 0.1%
2.2 1
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
< 0.1%
3.49 1
 
< 0.1%
3.5 2
< 0.1%
3.54 1
 
< 0.1%
3.85 3
< 0.1%
ValueCountFrequency (%)
13440 1
< 0.1%
7160 1
< 0.1%
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
5934.6 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4599.9 1
< 0.1%
4590 1
< 0.1%

sum_freight_value
Real number (ℝ)

Distinct7970
Distinct (%)8.1%
Missing775
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean22.823562
Minimum0
Maximum1794.96
Zeros338
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:18.883894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.88
Q113.85
median17.17
Q324.04
95-th percentile54.96
Maximum1794.96
Range1794.96
Interquartile range (IQR)10.19

Descriptive statistics

Standard deviation21.650909
Coefficient of variation (CV)0.94862098
Kurtosis565.34173
Mean22.823562
Median Absolute Deviation (MAD)4.38
Skewness12.052723
Sum2251909.5
Variance468.76188
MonotonicityNot monotonic
2023-02-14T10:33:19.417533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 2952
 
3.0%
7.78 1839
 
1.8%
14.1 1529
 
1.5%
11.85 1444
 
1.5%
18.23 1219
 
1.2%
7.39 1137
 
1.1%
15.23 823
 
0.8%
16.11 795
 
0.8%
8.72 766
 
0.8%
16.79 697
 
0.7%
Other values (7960) 85465
85.9%
(Missing) 775
 
0.8%
ValueCountFrequency (%)
0 338
0.3%
5.7 1
 
< 0.1%
5.82 1
 
< 0.1%
5.88 2
 
< 0.1%
6.52 1
 
< 0.1%
6.53 2
 
< 0.1%
6.56 1
 
< 0.1%
6.57 5
 
< 0.1%
6.78 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1794.96 1
< 0.1%
1002.29 1
< 0.1%
711.33 1
< 0.1%
626.64 1
< 0.1%
502.98 1
< 0.1%
497.42 1
< 0.1%
497.08 1
< 0.1%
479.28 1
< 0.1%
458.73 1
< 0.1%
456.47 1
< 0.1%

customer_unique_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct96096
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
8d50f5eadf50201ccdcedfb9e2ac8455
 
17
3e43e6105506432c953e165fb2acf44c
 
9
1b6c7548a2a1f9037c1fd3ddfed95f33
 
7
ca77025e7201e3b30c44b472ff346268
 
7
6469f99c1f9dfae7733b25662e7f1782
 
7
Other values (96091)
99394 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3182112
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93099 ?
Unique (%)93.6%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd rowaf07308b275d755c9edb36a90c618231
3rd row3a653a41f6f9fc3d2a113cf8398680e8
4th row7c142cf63193a1473d2e66489a9ae977
5th row72632f0f9dd73dfee390c9b22eb56dd6

Common Values

ValueCountFrequency (%)
8d50f5eadf50201ccdcedfb9e2ac8455 17
 
< 0.1%
3e43e6105506432c953e165fb2acf44c 9
 
< 0.1%
1b6c7548a2a1f9037c1fd3ddfed95f33 7
 
< 0.1%
ca77025e7201e3b30c44b472ff346268 7
 
< 0.1%
6469f99c1f9dfae7733b25662e7f1782 7
 
< 0.1%
de34b16117594161a6a89c50b289d35a 6
 
< 0.1%
47c1a3033b8b77b3ab6e109eb4d5fdf3 6
 
< 0.1%
63cfc61cee11cbe306bff5857d00bfe4 6
 
< 0.1%
12f5d6e1cbf93dafd9dcc19095df0b3d 6
 
< 0.1%
f0e310a6839dce9de1638e0fe5ab282a 6
 
< 0.1%
Other values (96086) 99364
99.9%

Length

2023-02-14T10:33:19.585412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8d50f5eadf50201ccdcedfb9e2ac8455 17
 
< 0.1%
3e43e6105506432c953e165fb2acf44c 9
 
< 0.1%
1b6c7548a2a1f9037c1fd3ddfed95f33 7
 
< 0.1%
6469f99c1f9dfae7733b25662e7f1782 7
 
< 0.1%
ca77025e7201e3b30c44b472ff346268 7
 
< 0.1%
de34b16117594161a6a89c50b289d35a 6
 
< 0.1%
47c1a3033b8b77b3ab6e109eb4d5fdf3 6
 
< 0.1%
63cfc61cee11cbe306bff5857d00bfe4 6
 
< 0.1%
12f5d6e1cbf93dafd9dcc19095df0b3d 6
 
< 0.1%
f0e310a6839dce9de1638e0fe5ab282a 6
 
< 0.1%
Other values (96086) 99364
99.9%

Most occurring characters

ValueCountFrequency (%)
6 199366
 
6.3%
8 199355
 
6.3%
1 199334
 
6.3%
a 199132
 
6.3%
d 199088
 
6.3%
b 199054
 
6.3%
5 199029
 
6.3%
0 199023
 
6.3%
2 198902
 
6.3%
e 198867
 
6.2%
Other values (6) 1190962
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1989082
62.5%
Lowercase Letter 1193030
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 199366
10.0%
8 199355
10.0%
1 199334
10.0%
5 199029
10.0%
0 199023
10.0%
2 198902
10.0%
9 198798
10.0%
3 198645
10.0%
4 198396
10.0%
7 198234
10.0%
Lowercase Letter
ValueCountFrequency (%)
a 199132
16.7%
d 199088
16.7%
b 199054
16.7%
e 198867
16.7%
f 198661
16.7%
c 198228
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1989082
62.5%
Latin 1193030
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 199366
10.0%
8 199355
10.0%
1 199334
10.0%
5 199029
10.0%
0 199023
10.0%
2 198902
10.0%
9 198798
10.0%
3 198645
10.0%
4 198396
10.0%
7 198234
10.0%
Latin
ValueCountFrequency (%)
a 199132
16.7%
d 199088
16.7%
b 199054
16.7%
e 198867
16.7%
f 198661
16.7%
c 198228
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3182112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 199366
 
6.3%
8 199355
 
6.3%
1 199334
 
6.3%
a 199132
 
6.3%
d 199088
 
6.3%
b 199054
 
6.3%
5 199029
 
6.3%
0 199023
 
6.3%
2 198902
 
6.3%
e 198867
 
6.2%
Other values (6) 1190962
37.4%

customer_zip_code_prefix
Real number (ℝ)

Distinct14994
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35137.475
Minimum1003
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:19.735982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3315
Q111347
median24416
Q358900
95-th percentile90550
Maximum99990
Range98987
Interquartile range (IQR)47553

Descriptive statistics

Standard deviation29797.939
Coefficient of variation (CV)0.84803872
Kurtosis-0.78820393
Mean35137.475
Median Absolute Deviation (MAD)16386
Skewness0.77902506
Sum3.4941056 × 109
Variance8.8791717 × 108
MonotonicityNot monotonic
2023-02-14T10:33:19.907239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22790 142
 
0.1%
24220 124
 
0.1%
22793 121
 
0.1%
24230 117
 
0.1%
22775 110
 
0.1%
29101 101
 
0.1%
13212 95
 
0.1%
35162 93
 
0.1%
22631 89
 
0.1%
38400 87
 
0.1%
Other values (14984) 98362
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 4
< 0.1%
1009 7
< 0.1%
1011 5
< 0.1%
1012 3
< 0.1%
1013 3
< 0.1%
ValueCountFrequency (%)
99990 1
 
< 0.1%
99980 2
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 2
 
< 0.1%
99955 3
 
< 0.1%
99950 9
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%

customer_city
Categorical

Distinct4119
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
sao paulo
15540 
rio de janeiro
 
6882
belo horizonte
 
2773
brasilia
 
2131
curitiba
 
1521
Other values (4114)
70594 

Length

Max length32
Median length27
Mean length10.344466
Min length3

Characters and Unicode

Total characters1028664
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1144 ?
Unique (%)1.2%

Sample

1st rowsao paulo
2nd rowbarreiras
3rd rowvianopolis
4th rowsao goncalo do amarante
5th rowsanto andre

Common Values

ValueCountFrequency (%)
sao paulo 15540
 
15.6%
rio de janeiro 6882
 
6.9%
belo horizonte 2773
 
2.8%
brasilia 2131
 
2.1%
curitiba 1521
 
1.5%
campinas 1444
 
1.5%
porto alegre 1379
 
1.4%
salvador 1245
 
1.3%
guarulhos 1189
 
1.2%
sao bernardo do campo 938
 
0.9%
Other values (4109) 64399
64.8%

Length

2023-02-14T10:33:20.087629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao 21050
 
12.1%
paulo 15606
 
9.0%
de 9684
 
5.6%
rio 8278
 
4.7%
janeiro 6882
 
3.9%
do 4276
 
2.5%
belo 2833
 
1.6%
horizonte 2798
 
1.6%
brasilia 2140
 
1.2%
porto 1648
 
0.9%
Other values (3285) 99118
56.9%

Most occurring characters

ValueCountFrequency (%)
a 169618
16.5%
o 126534
12.3%
i 78754
 
7.7%
r 76497
 
7.4%
74872
 
7.3%
e 67028
 
6.5%
s 62903
 
6.1%
n 45721
 
4.4%
u 44917
 
4.4%
l 44815
 
4.4%
Other values (21) 237005
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 953332
92.7%
Space Separator 74872
 
7.3%
Dash Punctuation 232
 
< 0.1%
Other Punctuation 226
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 169618
17.8%
o 126534
13.3%
i 78754
 
8.3%
r 76497
 
8.0%
e 67028
 
7.0%
s 62903
 
6.6%
n 45721
 
4.8%
u 44917
 
4.7%
l 44815
 
4.7%
p 37119
 
3.9%
Other values (16) 199426
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
74872
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 232
100.0%
Other Punctuation
ValueCountFrequency (%)
' 226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 953332
92.7%
Common 75332
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 169618
17.8%
o 126534
13.3%
i 78754
 
8.3%
r 76497
 
8.0%
e 67028
 
7.0%
s 62903
 
6.6%
n 45721
 
4.8%
u 44917
 
4.7%
l 44815
 
4.7%
p 37119
 
3.9%
Other values (16) 199426
20.9%
Common
ValueCountFrequency (%)
74872
99.4%
- 232
 
0.3%
' 226
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1028664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 169618
16.5%
o 126534
12.3%
i 78754
 
7.7%
r 76497
 
7.4%
74872
 
7.3%
e 67028
 
6.5%
s 62903
 
6.1%
n 45721
 
4.4%
u 44917
 
4.4%
l 44815
 
4.4%
Other values (21) 237005
23.0%

customer_state
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
SP
41746 
RJ
12852 
MG
11635 
RS
5466 
PR
5045 
Other values (22)
22697 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters198882
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowBA
3rd rowGO
4th rowRN
5th rowSP

Common Values

ValueCountFrequency (%)
SP 41746
42.0%
RJ 12852
 
12.9%
MG 11635
 
11.7%
RS 5466
 
5.5%
PR 5045
 
5.1%
SC 3637
 
3.7%
BA 3380
 
3.4%
DF 2140
 
2.2%
ES 2033
 
2.0%
GO 2020
 
2.0%
Other values (17) 9487
 
9.5%

Length

2023-02-14T10:33:20.223690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 41746
42.0%
rj 12852
 
12.9%
mg 11635
 
11.7%
rs 5466
 
5.5%
pr 5045
 
5.1%
sc 3637
 
3.7%
ba 3380
 
3.4%
df 2140
 
2.2%
es 2033
 
2.0%
go 2020
 
2.0%
Other values (17) 9487
 
9.5%

Most occurring characters

ValueCountFrequency (%)
S 53947
27.1%
P 50517
25.4%
R 24193
12.2%
M 14152
 
7.1%
G 13655
 
6.9%
J 12852
 
6.5%
A 5812
 
2.9%
E 5371
 
2.7%
C 5054
 
2.5%
B 3916
 
2.0%
Other values (7) 9413
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 198882
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 53947
27.1%
P 50517
25.4%
R 24193
12.2%
M 14152
 
7.1%
G 13655
 
6.9%
J 12852
 
6.5%
A 5812
 
2.9%
E 5371
 
2.7%
C 5054
 
2.5%
B 3916
 
2.0%
Other values (7) 9413
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 198882
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 53947
27.1%
P 50517
25.4%
R 24193
12.2%
M 14152
 
7.1%
G 13655
 
6.9%
J 12852
 
6.5%
A 5812
 
2.9%
E 5371
 
2.7%
C 5054
 
2.5%
B 3916
 
2.0%
Other values (7) 9413
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 53947
27.1%
P 50517
25.4%
R 24193
12.2%
M 14152
 
7.1%
G 13655
 
6.9%
J 12852
 
6.5%
A 5812
 
2.9%
E 5371
 
2.7%
C 5054
 
2.5%
B 3916
 
2.0%
Other values (7) 9413
 
4.7%

product_id
Categorical

Distinct31847
Distinct (%)32.3%
Missing775
Missing (%)0.8%
Memory size1.5 MiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
429
99a4788cb24856965c36a24e339b6058
 
427
422879e10f46682990de24d770e7f83d
 
339
d1c427060a0f73f6b889a5c7c61f2ac4
 
311
53b36df67ebb7c41585e8d54d6772e08
 
303
Other values (31842)
96857 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3157312
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18956 ?
Unique (%)19.2%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row595fac2a385ac33a80bd5114aec74eb8
3rd rowaa4383b373c6aca5d8797843e5594415
4th rowd0b61bfb1de832b15ba9d266ca96e5b0
5th row65266b2da20d04dbe00c5c2d3bb7859e

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 429
 
0.4%
99a4788cb24856965c36a24e339b6058 427
 
0.4%
422879e10f46682990de24d770e7f83d 339
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 311
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 303
 
0.3%
389d119b48cf3043d311335e499d9c6b 299
 
0.3%
368c6c730842d78016ad823897a372db 285
 
0.3%
154e7e31ebfa092203795c972e5804a6 269
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 264
 
0.3%
2b4609f8948be18874494203496bc318 258
 
0.3%
Other values (31837) 95482
96.0%
(Missing) 775
 
0.8%

Length

2023-02-14T10:33:20.342435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 429
 
0.4%
99a4788cb24856965c36a24e339b6058 427
 
0.4%
422879e10f46682990de24d770e7f83d 339
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 311
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 303
 
0.3%
389d119b48cf3043d311335e499d9c6b 299
 
0.3%
368c6c730842d78016ad823897a372db 285
 
0.3%
154e7e31ebfa092203795c972e5804a6 269
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 264
 
0.3%
2b4609f8948be18874494203496bc318 258
 
0.3%
Other values (31837) 95482
96.8%

Most occurring characters

ValueCountFrequency (%)
3 202985
 
6.4%
9 200585
 
6.4%
8 199130
 
6.3%
e 198981
 
6.3%
7 198253
 
6.3%
a 198185
 
6.3%
4 198184
 
6.3%
0 197884
 
6.3%
c 197489
 
6.3%
5 196954
 
6.2%
Other values (6) 1168682
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982405
62.8%
Lowercase Letter 1174907
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 202985
10.2%
9 200585
10.1%
8 199130
10.0%
7 198253
10.0%
4 198184
10.0%
0 197884
10.0%
5 196954
9.9%
2 196887
9.9%
6 196022
9.9%
1 195521
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 198981
16.9%
a 198185
16.9%
c 197489
16.8%
b 195448
16.6%
d 193791
16.5%
f 191013
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1982405
62.8%
Latin 1174907
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 202985
10.2%
9 200585
10.1%
8 199130
10.0%
7 198253
10.0%
4 198184
10.0%
0 197884
10.0%
5 196954
9.9%
2 196887
9.9%
6 196022
9.9%
1 195521
9.9%
Latin
ValueCountFrequency (%)
e 198981
16.9%
a 198185
16.9%
c 197489
16.8%
b 195448
16.6%
d 193791
16.5%
f 191013
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3157312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 202985
 
6.4%
9 200585
 
6.4%
8 199130
 
6.3%
e 198981
 
6.3%
7 198253
 
6.3%
a 198185
 
6.3%
4 198184
 
6.3%
0 197884
 
6.3%
c 197489
 
6.3%
5 196954
 
6.2%
Other values (6) 1168682
37.0%
Distinct2955
Distinct (%)3.0%
Missing775
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean782.638
Minimum0
Maximum3992
Zeros1420
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:20.490317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138
Q1341
median600
Q3988
95-th percentile2120
Maximum3992
Range3992
Interquartile range (IQR)647

Descriptive statistics

Standard deviation656.75087
Coefficient of variation (CV)0.83915025
Kurtosis4.7949182
Mean782.638
Median Absolute Deviation (MAD)300
Skewness1.9719271
Sum77219761
Variance431321.71
MonotonicityNot monotonic
2023-02-14T10:33:20.659593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1420
 
1.4%
1893 583
 
0.6%
492 539
 
0.5%
341 536
 
0.5%
903 479
 
0.5%
245 478
 
0.5%
348 459
 
0.5%
236 428
 
0.4%
366 394
 
0.4%
575 361
 
0.4%
Other values (2945) 92989
93.5%
(Missing) 775
 
0.8%
ValueCountFrequency (%)
0 1420
1.4%
4 6
 
< 0.1%
8 1
 
< 0.1%
15 1
 
< 0.1%
20 6
 
< 0.1%
26 2
 
< 0.1%
27 3
 
< 0.1%
28 2
 
< 0.1%
30 7
 
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 3
< 0.1%
3963 1
 
< 0.1%
3956 2
< 0.1%
3954 2
< 0.1%
3950 1
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing775
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.217765
Minimum0
Maximum20
Zeros1420
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:20.830628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7535453
Coefficient of variation (CV)0.79068131
Kurtosis4.4497692
Mean2.217765
Median Absolute Deviation (MAD)1
Skewness1.8258834
Sum218818
Variance3.0749212
MonotonicityNot monotonic
2023-02-14T10:33:20.968364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 48050
48.3%
2 19157
 
19.3%
3 11212
 
11.3%
4 7587
 
7.6%
5 4980
 
5.0%
6 3397
 
3.4%
0 1420
 
1.4%
7 1405
 
1.4%
8 683
 
0.7%
10 321
 
0.3%
Other values (10) 454
 
0.5%
(Missing) 775
 
0.8%
ValueCountFrequency (%)
0 1420
 
1.4%
1 48050
48.3%
2 19157
 
19.3%
3 11212
 
11.3%
4 7587
 
7.6%
5 4980
 
5.0%
6 3397
 
3.4%
7 1405
 
1.4%
8 683
 
0.7%
9 289
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 8
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 26
 
< 0.1%
12 44
 
< 0.1%
11 62
 
0.1%
10 321
0.3%

product_weight_g
Real number (ℝ)

Distinct2190
Distinct (%)2.2%
Missing791
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2101.1762
Minimum0
Maximum40425
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:21.151977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9750
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3763.2039
Coefficient of variation (CV)1.7909987
Kurtosis16.417829
Mean2101.1762
Median Absolute Deviation (MAD)500
Skewness3.6112048
Sum2.0728104 × 108
Variance14161704
MonotonicityNot monotonic
2023-02-14T10:33:21.324805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 5927
 
6.0%
150 4636
 
4.7%
250 3998
 
4.0%
300 3735
 
3.8%
400 3186
 
3.2%
100 3112
 
3.1%
350 2821
 
2.8%
500 2362
 
2.4%
600 2284
 
2.3%
700 1751
 
1.8%
Other values (2180) 64838
65.2%
ValueCountFrequency (%)
0 6
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 841
0.8%
53 2
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 8
 
< 0.1%
61 4
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 255
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 3
 
< 0.1%
29600 5
 
< 0.1%
29500 1
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing791
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean30.09779
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:21.508288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.125854
Coefficient of variation (CV)0.53578201
Kurtosis3.7918662
Mean30.09779
Median Absolute Deviation (MAD)8
Skewness1.7714081
Sum2969147
Variance260.04318
MonotonicityNot monotonic
2023-02-14T10:33:21.672733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 15278
 
15.4%
20 9154
 
9.2%
30 6324
 
6.4%
17 5339
 
5.4%
18 5164
 
5.2%
19 4141
 
4.2%
25 4130
 
4.2%
40 3568
 
3.6%
22 3435
 
3.5%
35 2590
 
2.6%
Other values (89) 39527
39.7%
ValueCountFrequency (%)
7 30
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 7
 
< 0.1%
11 82
 
0.1%
12 34
 
< 0.1%
13 50
 
0.1%
14 119
 
0.1%
15 178
 
0.2%
16 15278
15.4%
ValueCountFrequency (%)
105 301
0.3%
104 29
 
< 0.1%
103 35
 
< 0.1%
102 42
 
< 0.1%
101 88
 
0.1%
100 310
0.3%
99 33
 
< 0.1%
98 42
 
< 0.1%
97 10
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing791
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean16.479078
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:21.851692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile44
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.310002
Coefficient of variation (CV)0.80769096
Kurtosis7.4745489
Mean16.479078
Median Absolute Deviation (MAD)6
Skewness2.2576774
Sum1625661
Variance177.15615
MonotonicityNot monotonic
2023-02-14T10:33:22.024534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 8514
 
8.6%
20 5857
 
5.9%
15 5665
 
5.7%
12 5640
 
5.7%
11 5482
 
5.5%
2 4438
 
4.5%
4 4223
 
4.2%
8 4063
 
4.1%
16 3991
 
4.0%
5 3920
 
3.9%
Other values (92) 46857
47.1%
ValueCountFrequency (%)
2 4438
4.5%
3 2340
 
2.4%
4 4223
4.2%
5 3920
3.9%
6 3027
 
3.0%
7 3714
3.7%
8 4063
4.1%
9 2804
 
2.8%
10 8514
8.6%
11 5482
5.5%
ValueCountFrequency (%)
105 109
0.1%
104 12
 
< 0.1%
103 37
 
< 0.1%
102 7
 
< 0.1%
100 39
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 21
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct95
Distinct (%)0.1%
Missing791
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean23.02002
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-14T10:33:22.201963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.746284
Coefficient of variation (CV)0.51026386
Kurtosis4.626345
Mean23.02002
Median Absolute Deviation (MAD)6
Skewness1.7227086
Sum2270925
Variance137.9752
MonotonicityNot monotonic
2023-02-14T10:33:22.377248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 10480
 
10.5%
11 9185
 
9.2%
15 7911
 
8.0%
16 7387
 
7.4%
30 6427
 
6.5%
12 4846
 
4.9%
13 4683
 
4.7%
14 4079
 
4.1%
18 3566
 
3.6%
40 3383
 
3.4%
Other values (85) 36703
36.9%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 16
 
< 0.1%
9 48
 
< 0.1%
10 68
 
0.1%
11 9185
9.2%
12 4846
4.9%
13 4683
4.7%
14 4079
4.1%
15 7911
8.0%
ValueCountFrequency (%)
118 7
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 41
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%
Distinct72
Distinct (%)0.1%
Missing775
Missing (%)0.8%
Memory size1.5 MiB
bed_bath_table
9301 
health_beauty
8803 
sports_leisure
7681 
computers_accessories
6659 
furniture_decor
6358 
Other values (67)
59864 

Length

Max length39
Median length31
Mean length12.770934
Min length3

Characters and Unicode

Total characters1260057
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowperfumery
3rd rowauto
4th rowpet_shop
5th rowstationery

Common Values

ValueCountFrequency (%)
bed_bath_table 9301
 
9.4%
health_beauty 8803
 
8.9%
sports_leisure 7681
 
7.7%
computers_accessories 6659
 
6.7%
furniture_decor 6358
 
6.4%
housewares 5820
 
5.9%
watches_gifts 5607
 
5.6%
telephony 4189
 
4.2%
auto 3878
 
3.9%
toys 3851
 
3.9%
Other values (62) 36519
36.7%

Length

2023-02-14T10:33:22.555329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bed_bath_table 9301
 
9.4%
health_beauty 8803
 
8.9%
sports_leisure 7681
 
7.8%
computers_accessories 6659
 
6.7%
furniture_decor 6358
 
6.4%
housewares 5820
 
5.9%
watches_gifts 5607
 
5.7%
telephony 4189
 
4.2%
auto 3878
 
3.9%
toys 3851
 
3.9%
Other values (62) 36519
37.0%

Most occurring characters

ValueCountFrequency (%)
e 152705
12.1%
s 119636
 
9.5%
t 110914
 
8.8%
o 93648
 
7.4%
a 85653
 
6.8%
r 84434
 
6.7%
_ 83451
 
6.6%
u 64761
 
5.1%
c 59784
 
4.7%
i 51976
 
4.1%
Other values (15) 353095
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1176350
93.4%
Connector Punctuation 83451
 
6.6%
Decimal Number 256
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 152705
13.0%
s 119636
 
10.2%
t 110914
 
9.4%
o 93648
 
8.0%
a 85653
 
7.3%
r 84434
 
7.2%
u 64761
 
5.5%
c 59784
 
5.1%
i 51976
 
4.4%
h 50327
 
4.3%
Other values (13) 302512
25.7%
Connector Punctuation
ValueCountFrequency (%)
_ 83451
100.0%
Decimal Number
ValueCountFrequency (%)
2 256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1176350
93.4%
Common 83707
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 152705
13.0%
s 119636
 
10.2%
t 110914
 
9.4%
o 93648
 
8.0%
a 85653
 
7.3%
r 84434
 
7.2%
u 64761
 
5.5%
c 59784
 
5.1%
i 51976
 
4.4%
h 50327
 
4.3%
Other values (13) 302512
25.7%
Common
ValueCountFrequency (%)
_ 83451
99.7%
2 256
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1260057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 152705
12.1%
s 119636
 
9.5%
t 110914
 
8.8%
o 93648
 
7.4%
a 85653
 
6.8%
r 84434
 
6.7%
_ 83451
 
6.6%
u 64761
 
5.1%
c 59784
 
4.7%
i 51976
 
4.1%
Other values (15) 353095
28.0%

Interactions

2023-02-14T10:33:07.939198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:26.588643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:29.228524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:32.437634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:35.068668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:38.359052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:41.075340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:43.734351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:46.701381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:49.309239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:52.225126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:55.430903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:58.269265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:01.395937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:04.959168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:08.089008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:26.802163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:29.424815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:32.646866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:35.222878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:38.545550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:41.227992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:44.097765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:46.852822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:49.473710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:52.387854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:55.633376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:58.491822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:01.552751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:05.160390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:08.276086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:26.966313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:29.624260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:32.798497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:35.373656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:38.699480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:41.400594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:44.272662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:47.013349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:49.629938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:52.542739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:55.832744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:58.754936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:01.796223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:05.313575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:08.445961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:27.125406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:29.814431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:32.958019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:35.623237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:38.864820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:41.558396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:44.442874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:47.174799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:49.810706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:52.712001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:56.111272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:59.029287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:02.093431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:05.477641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:08.639992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:27.277516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:30.111640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:33.120241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:35.848561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:39.078129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:41.709972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:44.591061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:47.344869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:49.973993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:52.906008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:56.277350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:59.285848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:02.352989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:05.651819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:08.842236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:27.458229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:30.368429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:33.313124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:36.040827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:39.257593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:41.877088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:44.748542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:47.514522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:50.159914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:53.427610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:56.460158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:59.483423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:02.621582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:05.812517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:09.035935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:27.668971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:30.558245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:33.493948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:36.214811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:39.428532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:42.053341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:44.916209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:47.720075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:50.373930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:53.717105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:56.652468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:59.731025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:02.901968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:06.324856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:09.197081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:27.879236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:30.733027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:33.660478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:36.587870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:39.619075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:42.224808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:45.075504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:47.980504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:50.556149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:53.900826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:56.845618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:59.914177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:03.166223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:06.500213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:09.383487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:28.046540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:30.916698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:33.829650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:36.765909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:39.781696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:42.389638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:45.257944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:48.149682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:50.757432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:54.093906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:57.024251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:00.105903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:03.431162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:06.689399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:09.556667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:28.196727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:31.077641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:33.991866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:36.941534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:39.939862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:42.543808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:45.434527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:48.320182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:50.929206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:54.243048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:57.196107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:00.280882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:03.677224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:06.919514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:09.741591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:28.402119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:31.296185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:34.180217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:37.123210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:40.111415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:42.732242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:45.657070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:48.497858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:51.214122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:54.407774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:57.369589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:00.495994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:03.925912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:07.076680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:09.919258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:28.561397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:31.563156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:34.367393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:37.313164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:40.278099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:42.907498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:45.909977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:48.677655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:51.415572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:54.576424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:57.544492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:00.676697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:04.196251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:07.255123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:10.112736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:28.727197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:31.838430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:34.551506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:37.580264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:40.456746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:43.073027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:46.189089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:48.847218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:51.625972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:54.785310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:57.726336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:00.858669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:04.374554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:07.477074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:10.275909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:28.896649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:31.995483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:34.711557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:37.841763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:40.636578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:43.245662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:46.373029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:49.000744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:51.796258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:55.004647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:57.890223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:01.022409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:04.527278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:07.632783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:10.454165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:29.058299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:32.179397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:34.879207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:38.094090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:40.877729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:43.496669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:46.530664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:49.156473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:51.991961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:55.184670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:32:58.076297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:01.214770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:04.703341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T10:33:07.785885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-14T10:33:22.727937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
length_comment_titlelength_comment_messagepayment_sequentialpayment_installmentspayment_valuenb_itemssum_pricesum_freight_valuecustomer_zip_code_prefixproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmorder_statusreview_scorepayment_typecustomer_stateproduct_category_name_english
length_comment_title1.0000.313-0.0060.0040.0330.0260.0280.053-0.0160.0300.006-0.011-0.032-0.003-0.0200.0210.0800.0280.0130.034
length_comment_message0.3131.0000.0050.0450.0680.0820.0620.0700.015-0.007-0.0050.0370.0140.0220.0150.0480.2060.0080.0210.020
payment_sequential-0.0060.0051.000-0.063-0.008-0.004-0.0100.0070.006-0.0100.0010.0100.0130.0010.0140.0100.0000.1080.0050.000
payment_installments0.0040.045-0.0631.0000.3820.0570.3750.2310.0700.0370.0040.2200.1190.1220.1370.0050.0200.1820.0330.090
payment_value0.0330.068-0.0080.3821.0000.2210.9900.5660.1120.1920.0080.5190.2680.3480.2750.0120.0140.0060.0150.100
nb_items0.0260.082-0.0040.0570.2211.0000.1770.377-0.008-0.037-0.056-0.0040.0080.0040.0010.0000.0310.0090.0000.027
sum_price0.0280.062-0.0100.3750.9900.1771.0000.4690.0650.1960.0120.5060.2560.3400.2640.0090.0120.0080.0130.092
sum_freight_value0.0530.0700.0070.2310.5660.3770.4691.0000.4270.100-0.0090.4190.2730.2720.2620.0000.0150.0000.0300.054
customer_zip_code_prefix-0.0160.0150.0060.0700.112-0.0080.0650.4271.0000.0270.0250.0250.0130.014-0.0020.0210.0420.0240.8960.047
product_description_lenght0.030-0.007-0.0100.0370.192-0.0370.1960.1000.0271.0000.1550.100-0.0110.132-0.0600.0030.0110.0120.0190.212
product_photos_qty0.006-0.0050.0010.0040.008-0.0560.012-0.0090.0250.1551.0000.0140.009-0.068-0.0040.0130.0110.0000.0130.150
product_weight_g-0.0110.0370.0100.2200.519-0.0040.5060.4190.0250.1000.0141.0000.6200.5360.6220.0040.0190.0110.0130.192
product_length_cm-0.0320.0140.0130.1190.2680.0080.2560.2730.013-0.0110.0090.6201.0000.2600.6390.0080.0140.0120.0110.260
product_height_cm-0.0030.0220.0010.1220.3480.0040.3400.2720.0140.132-0.0680.5360.2601.0000.3460.0110.0140.0110.0130.266
product_width_cm-0.0200.0150.0140.1370.2750.0010.2640.262-0.002-0.060-0.0040.6220.6390.3461.0000.0000.0110.0100.0120.290
order_status0.0210.0480.0100.0050.0120.0000.0090.0000.0210.0030.0130.0040.0080.0110.0001.0000.1650.0390.0230.023
review_score0.0800.2060.0000.0200.0140.0310.0120.0150.0420.0110.0110.0190.0140.0140.0110.1651.0000.0100.0480.046
payment_type0.0280.0080.1080.1820.0060.0090.0080.0000.0240.0120.0000.0110.0120.0110.0100.0390.0101.0000.0260.036
customer_state0.0130.0210.0050.0330.0150.0000.0130.0300.8960.0190.0130.0130.0110.0130.0120.0230.0480.0261.0000.030
product_category_name_english0.0340.0200.0000.0900.1000.0270.0920.0540.0470.2120.1500.1920.2600.2660.2900.0230.0460.0360.0301.000

Missing values

2023-02-14T10:33:11.037216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-14T10:33:12.167950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-14T10:33:13.560664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

order_idcustomer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datereview_scorelength_comment_titlelength_comment_messagereview_answer_timestamppayment_typepayment_sequentialpayment_installmentspayment_valueproduct_most_frequentnb_itemssum_pricesum_freight_valuecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateproduct_idproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_english
0e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:004.00.0170.02017-10-12 03:43:48credit_card,voucher3.01.038.7187285b34884572647811a353c7ac498a1.029.998.727c396fd4830fd04220f754e42b4e5bff3149sao pauloSP87285b34884572647811a353c7ac498a268.04.0500.019.08.013.0housewares
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